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Firstly, the multi-resolution analysis of single and compound voltage sag disturbance signals is carried out by using AST which can adjust the window width factor adaptively. Then, eight feature quantities of disturbance are extracted from the AST modulus matrix according to the characteristic differences of the above disturbances. Finally, a chaotic ensemble decision tree is constructed by combining the ergodicity advantage of Tent chaotic search and the \u201ccollective intelligence\u201d of ensemble decision tree to accomplish the effective identification of voltage sag disturbance sources. The simulation results manifest that the proposed method can accurately identify the voltage sag disturbance source signals under different noise environments, and its classification accuracy is higher than that of traditional decision trees, weighted nearest neighbors method, support vector machines and probabilistic neural networks. The new method has good noise resistance and robustness.<\/jats:p>","DOI":"10.1177\/14727978241299651","type":"journal-article","created":{"date-parts":[[2025,4,28]],"date-time":"2025-04-28T10:26:26Z","timestamp":1745835986000},"page":"130-145","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":1,"title":["Voltage sag source classification identification based on AST-CEDT"],"prefix":"10.1177","volume":"25","author":[{"given":"Min","family":"Zhao","sequence":"first","affiliation":[{"name":"Henan Polytechnic University"},{"name":"Hebi Polytechnic"}]},{"given":"Zhihui","family":"Kang","sequence":"additional","affiliation":[{"name":"Henan Polytechnic University"},{"name":"Hebi Polytechnic"}]}],"member":"179","published-online":{"date-parts":[[2024,11,9]]},"reference":[{"issue":"9","key":"e_1_3_2_2_2","first-page":"3021","article-title":"Morphological characteristics and technology prospect of new distribution system","volume":"47","author":"Xuzhu D","year":"2021","unstructured":"Xuzhu D, Zhuhu H, Lei S, et al. 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